Software Cost Estimation using Stacked Ensemble Classifier and Feature Selection

Predicting the cost of the development effort is essential for successful projects. This helps software project managers to allocate resources, and determine budget or delivery date. This paper evaluates a set of machine learning algorithms and techniques in predicting the development cost of software projects. A feature selection algorithm is utilized to enhance the accuracy of the prediction process. A set of evaluations are presented based on basic classifiers and stacked ensemble classifiers with and without the feature selection approach. The evaluation study uses a dataset from 76 university students' software projects. Results show that using a stacked ensemble classifier and feature selection technique can increase the accuracy of software cost prediction models.